PT  - JOURNAL ARTICLE
AU  - Joshua D. Welch
AU  - Alexander Hartemink
AU  - Jan F. Prins
TI  - SLICER: Inferring Branched, Nonlinear Cellular Trajectories from Single Cell RNA-seq Data
AID  - 10.1101/047845
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 047845
4099  - http://biorxiv.org/content/early/2016/04/09/047845.short
4100  - http://biorxiv.org/content/early/2016/04/09/047845.full
AB  - Single cell experiments provide an unprecedented opportunity to reconstruct a sequence of changes in a biological process from individual “snapshots” of cells. However, nonlinear gene expression changes, genes unrelated to the process, and the possibility of branching trajectories make this a challenging problem. We developed SLICER (Selective Locally Linear Inference of Cellular Expression Relationships) to address these challenges. SLICER can infer highly nonlinear trajectories, select genes without prior knowledge of the process, and automatically determine the location and number of branches and loops. SLICER more accurately recovers the ordering of points along simulated trajectories than existing methods. We demonstrate the effectiveness of SLICER on previously published data from mouse lung cells and neural stem cells.